{"id":"W2069818616","doi":"10.1016/s1053-8119(03)00121-6","title":"The impact of individual differences on the neural circuitry underlying sadness","year":2003,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":109,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Université de Montréal","keywords":"Sadness; Psychology; Insula; Functional magnetic resonance imaging; Neuroimaging; Neural correlates of consciousness; Generalizability theory; Orbitofrontal cortex; Functional neuroimaging; Neural activity; Cognitive psychology; Statistical parametric mapping; Audiology; Developmental psychology; Prefrontal cortex; Neuroscience; Anger; Clinical psychology; Magnetic resonance imaging; Medicine; Cognition","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003866226,0.0001752643,0.0001525741,0.00004566628,0.0008018137,0.0001432025,0.0004996755,0.00002799197,0.0000395848],"category_scores_gemma":[0.0138411,0.00008597071,0.0001462314,0.000339149,0.0004508382,0.0001180896,0.00009136595,0.0003296724,0.00002011573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002567635,"about_ca_system_score_gemma":0.00005748873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001600006,"about_ca_topic_score_gemma":0.000005424731,"domain_scores_codex":[0.9980206,0.0006391348,0.0001729234,0.0003543418,0.0005064072,0.0003065286],"domain_scores_gemma":[0.9816596,0.01773703,0.0001203321,0.0004116149,0.00003358296,0.00003785736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000243235,0.0007459418,0.07655635,0.0000403111,0.0001562006,0.000101117,0.002585034,0.0008430406,0.7371975,0.1410732,0.02330545,0.01715273],"study_design_scores_gemma":[0.0003994447,0.0006935713,0.9492261,0.0000158267,0.00001933334,0.00006106606,0.0004846169,0.000412939,0.03771166,0.01015148,0.0005754598,0.0002484602],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876683,0.00003678388,0.00001586577,0.002275892,0.0004608611,0.0002178565,0.00002279206,0.00004218433,0.00925946],"genre_scores_gemma":[0.9982243,0.00001541469,0.00000142771,0.00153828,0.00002993104,0.00001868253,1.901976e-7,0.0000157534,0.0001560429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8726698,"threshold_uncertainty_score":0.9944658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1712631351901523,"score_gpt":0.3321399527293124,"score_spread":0.1608768175391601,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}